


"""simple docstring"""






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import YolosImageProcessor




class 							A_      (       unittest.TestCase ):


  def __init__(			self:						Dict							,__lowerCAmelCase:						Tuple							,__lowerCAmelCase:						List[str]=7							,__lowerCAmelCase:						int=3							,__lowerCAmelCase:						Union[str, Any]=30							,__lowerCAmelCase:						List[str]=400							,__lowerCAmelCase:						List[Any]=True							,__lowerCAmelCase:						List[Any]=None							,__lowerCAmelCase:						List[str]=True							,__lowerCAmelCase:						int=[0.5, 0.5, 0.5]							,__lowerCAmelCase:						int=[0.5, 0.5, 0.5]							,__lowerCAmelCase:						List[Any]=True							,__lowerCAmelCase:						Any=1 / 255							,__lowerCAmelCase:						Optional[int]=True							,):







     '''simple docstring'''







     _lowerCamelCase   :				List[Any]							=     size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
     _lowerCamelCase   :				int							=     parent
     _lowerCamelCase   :				List[str]							=     batch_size
     _lowerCamelCase   :				Optional[Any]							=     num_channels
     _lowerCamelCase   :				Optional[int]							=     min_resolution
     _lowerCamelCase   :				str							=     max_resolution
     _lowerCamelCase   :				List[Any]							=     do_resize
     _lowerCamelCase   :				str							=     size
     _lowerCamelCase   :				List[Any]							=     do_normalize
     _lowerCamelCase   :				str							=     image_mean
     _lowerCamelCase   :				Optional[Any]							=     image_std
     _lowerCamelCase   :				Tuple							=     do_rescale
     _lowerCamelCase   :				str							=     rescale_factor
     _lowerCamelCase   :				Dict							=     do_pad


  def 							_lowercase					(			self:						List[str]    ):







     '''simple docstring'''







     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_rescale": self.do_rescale,
         "rescale_factor": self.rescale_factor,
         "do_pad": self.do_pad,
     }






  def 							_lowercase					(			self:						Tuple							,__lowerCAmelCase:						Tuple							,__lowerCAmelCase:						str=False    ):







     '''simple docstring'''







     if not batched:
        _lowerCamelCase   :				Optional[Any]							=     image_inputs[0]
        if isinstance(__lowerCAmelCase							,Image.Image    ):
           _lowerCamelCase,					_lowerCamelCase   :				Optional[int]							=     image.size
        else:
           _lowerCamelCase,					_lowerCamelCase   :				List[str]							=     image.shape[1], image.shape[2]
        if w < h:
           _lowerCamelCase   :				Any							=     int(self.size["shortest_edge"] * h / w    )
           _lowerCamelCase   :				List[Any]							=     self.size["shortest_edge"]
        elif w > h:
           _lowerCamelCase   :				str							=     self.size["shortest_edge"]
           _lowerCamelCase   :				Dict							=     int(self.size["shortest_edge"] * w / h    )
        else:
           _lowerCamelCase   :				Union[str, Any]							=     self.size["shortest_edge"]
           _lowerCamelCase   :				Any							=     self.size["shortest_edge"]

     else:
        _lowerCamelCase   :				Union[str, Any]							=     []
        for image in image_inputs:
           _lowerCamelCase,					_lowerCamelCase   :				List[Any]							=     self.get_expected_values([image]    )
           expected_values.append((expected_height, expected_width)    )
        _lowerCamelCase   :				Optional[int]							=     max(__lowerCAmelCase							,key=lambda __lowerCAmelCase    : item[0]    )[0]
        _lowerCamelCase   :				int							=     max(__lowerCAmelCase							,key=lambda __lowerCAmelCase    : item[1]    )[1]

     return expected_height, expected_width




@require_torch
@require_vision
class 							A_      (       _a     ,	unittest.TestCase ):
  lowerCAmelCase__      			=	YolosImageProcessor if is_vision_available() else None


  def 							_lowercase					(			self:						Any    ):







     '''simple docstring'''







     _lowerCamelCase   :				Tuple							=     YolosImageProcessingTester(self    )


  @property
  def 							_lowercase					(			self:						List[Any]    ):







     '''simple docstring'''







     return self.image_processor_tester.prepare_image_processor_dict()


  def 							_lowercase					(			self:						Union[str, Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				Tuple							=     self.image_processing_class(**self.image_processor_dict    )
     self.assertTrue(hasattr(__lowerCAmelCase							,"image_mean"    )    )
     self.assertTrue(hasattr(__lowerCAmelCase							,"image_std"    )    )
     self.assertTrue(hasattr(__lowerCAmelCase							,"do_normalize"    )    )
     self.assertTrue(hasattr(__lowerCAmelCase							,"do_resize"    )    )
     self.assertTrue(hasattr(__lowerCAmelCase							,"size"    )    )


  def 							_lowercase					(			self:						str    ):







     '''simple docstring'''







     _lowerCamelCase   :				Optional[int]							=     self.image_processing_class.from_dict(self.image_processor_dict    )
     self.assertEqual(image_processor.size							,{"shortest_edge": 18, "longest_edge": 1_333}    )
     self.assertEqual(image_processor.do_pad							,__lowerCAmelCase    )

     _lowerCamelCase   :				Any							=     self.image_processing_class.from_dict(
         self.image_processor_dict							,size=42							,max_size=84							,pad_and_return_pixel_mask=__lowerCAmelCase    )
     self.assertEqual(image_processor.size							,{"shortest_edge": 42, "longest_edge": 84}    )
     self.assertEqual(image_processor.do_pad							,__lowerCAmelCase    )


  def 							_lowercase					(			self:						List[str]    ):







     '''simple docstring'''







     pass


  def 							_lowercase					(			self:						int    ):







     '''simple docstring'''







     _lowerCamelCase   :				Tuple							=     self.image_processing_class(**self.image_processor_dict    )
     # create random PIL images
     _lowerCamelCase   :				List[str]							=     prepare_image_inputs(self.image_processor_tester							,equal_resolution=__lowerCAmelCase    )
     for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase							,Image.Image    )

     # Test not batched input
     _lowerCamelCase   :				str							=     image_processing(image_inputs[0]							,return_tensors="pt"    ).pixel_values

     _lowerCamelCase,					_lowerCamelCase   :				List[Any]							=     self.image_processor_tester.get_expected_values(__lowerCAmelCase    )

     self.assertEqual(
         encoded_images.shape							,(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,)

     # Test batched
     _lowerCamelCase,					_lowerCamelCase   :				str							=     self.image_processor_tester.get_expected_values(__lowerCAmelCase							,batched=__lowerCAmelCase    )

     _lowerCamelCase   :				Any							=     image_processing(__lowerCAmelCase							,return_tensors="pt"    ).pixel_values
     self.assertEqual(
         encoded_images.shape							,(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )							,)


  def 							_lowercase					(			self:						int    ):







     '''simple docstring'''







     _lowerCamelCase   :				Union[str, Any]							=     self.image_processing_class(**self.image_processor_dict    )
     # create random numpy tensors
     _lowerCamelCase   :				List[str]							=     prepare_image_inputs(self.image_processor_tester							,equal_resolution=__lowerCAmelCase							,numpify=__lowerCAmelCase    )
     for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase							,np.ndarray    )

     # Test not batched input
     _lowerCamelCase   :				List[Any]							=     image_processing(image_inputs[0]							,return_tensors="pt"    ).pixel_values

     _lowerCamelCase,					_lowerCamelCase   :				Any							=     self.image_processor_tester.get_expected_values(__lowerCAmelCase    )

     self.assertEqual(
         encoded_images.shape							,(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,)

     # Test batched
     _lowerCamelCase   :				Tuple							=     image_processing(__lowerCAmelCase							,return_tensors="pt"    ).pixel_values

     _lowerCamelCase,					_lowerCamelCase   :				Optional[int]							=     self.image_processor_tester.get_expected_values(__lowerCAmelCase							,batched=__lowerCAmelCase    )

     self.assertEqual(
         encoded_images.shape							,(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )							,)


  def 							_lowercase					(			self:						List[Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				Union[str, Any]							=     self.image_processing_class(**self.image_processor_dict    )
     # create random PyTorch tensors
     _lowerCamelCase   :				List[Any]							=     prepare_image_inputs(self.image_processor_tester							,equal_resolution=__lowerCAmelCase							,torchify=__lowerCAmelCase    )
     for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase							,torch.Tensor    )

     # Test not batched input
     _lowerCamelCase   :				Dict							=     image_processing(image_inputs[0]							,return_tensors="pt"    ).pixel_values

     _lowerCamelCase,					_lowerCamelCase   :				Optional[Any]							=     self.image_processor_tester.get_expected_values(__lowerCAmelCase    )

     self.assertEqual(
         encoded_images.shape							,(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,)

     # Test batched
     _lowerCamelCase   :				str							=     image_processing(__lowerCAmelCase							,return_tensors="pt"    ).pixel_values

     _lowerCamelCase,					_lowerCamelCase   :				Tuple							=     self.image_processor_tester.get_expected_values(__lowerCAmelCase							,batched=__lowerCAmelCase    )

     self.assertEqual(
         encoded_images.shape							,(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )							,)


  def 							_lowercase					(			self:						str    ):







     '''simple docstring'''







     _lowerCamelCase   :				int							=     self.image_processing_class(**self.image_processor_dict    )
     _lowerCamelCase   :				Any							=     self.image_processing_class(do_resize=__lowerCAmelCase							,do_normalize=__lowerCAmelCase							,do_rescale=__lowerCAmelCase    )
     # create random PyTorch tensors
     _lowerCamelCase   :				Any							=     prepare_image_inputs(self.image_processor_tester							,equal_resolution=__lowerCAmelCase							,torchify=__lowerCAmelCase    )
     for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase							,torch.Tensor    )

     # Test whether the method "pad" and calling the image processor return the same tensors
     _lowerCamelCase   :				List[Any]							=     image_processing_a.pad(__lowerCAmelCase							,return_tensors="pt"    )
     _lowerCamelCase   :				List[str]							=     image_processing_a(__lowerCAmelCase							,return_tensors="pt"    )

     self.assertTrue(
         torch.allclose(encoded_images_with_method["pixel_values"]							,encoded_images["pixel_values"]							,atol=1e-4    )    )


  @slow
  def 							_lowercase					(			self:						str    ):







     '''simple docstring'''







     _lowerCamelCase   :				Union[str, Any]							=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
     with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"							,"r"    ) as f:
        _lowerCamelCase   :				Optional[Any]							=     json.loads(f.read()    )

     _lowerCamelCase   :				Optional[Any]							=     {"image_id": 39_769, "annotations": target}

     # encode them
     _lowerCamelCase   :				int							=     YolosImageProcessor.from_pretrained("hustvl/yolos-small"    )
     _lowerCamelCase   :				Optional[Any]							=     image_processing(images=__lowerCAmelCase							,annotations=__lowerCAmelCase							,return_tensors="pt"    )

     # verify pixel values
     _lowerCamelCase   :				Tuple							=     torch.Size([1, 3, 800, 1_066]    )
     self.assertEqual(encoding["pixel_values"].shape							,__lowerCAmelCase    )

     _lowerCamelCase   :				List[str]							=     torch.tensor([0.27_96, 0.31_38, 0.34_81]    )
     self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,__lowerCAmelCase							,atol=1e-4    )    )

     # verify area
     _lowerCamelCase   :				Tuple							=     torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]    )
     self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,__lowerCAmelCase    )    )
     # verify boxes
     _lowerCamelCase   :				List[str]							=     torch.Size([6, 4]    )
     self.assertEqual(encoding["labels"][0]["boxes"].shape							,__lowerCAmelCase    )
     _lowerCamelCase   :				Dict							=     torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]    )
     self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,__lowerCAmelCase							,atol=1e-3    )    )
     # verify image_id
     _lowerCamelCase   :				List[str]							=     torch.tensor([39_769]    )
     self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,__lowerCAmelCase    )    )
     # verify is_crowd
     _lowerCamelCase   :				Any							=     torch.tensor([0, 0, 0, 0, 0, 0]    )
     self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,__lowerCAmelCase    )    )
     # verify class_labels
     _lowerCamelCase   :				Optional[int]							=     torch.tensor([75, 75, 63, 65, 17, 17]    )
     self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,__lowerCAmelCase    )    )
     # verify orig_size
     _lowerCamelCase   :				Dict							=     torch.tensor([480, 640]    )
     self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,__lowerCAmelCase    )    )
     # verify size
     _lowerCamelCase   :				Dict							=     torch.tensor([800, 1_066]    )
     self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,__lowerCAmelCase    )    )






  @slow
  def 							_lowercase					(			self:						Tuple    ):







     '''simple docstring'''







     _lowerCamelCase   :				Dict							=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
     with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"							,"r"    ) as f:
        _lowerCamelCase   :				Optional[int]							=     json.loads(f.read()    )

     _lowerCamelCase   :				Optional[int]							=     {"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

     _lowerCamelCase   :				List[Any]							=     pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"    )

     # encode them
     _lowerCamelCase   :				Optional[Any]							=     YolosImageProcessor(format="coco_panoptic"    )
     _lowerCamelCase   :				Any							=     image_processing(images=__lowerCAmelCase							,annotations=__lowerCAmelCase							,masks_path=__lowerCAmelCase							,return_tensors="pt"    )

     # verify pixel values
     _lowerCamelCase   :				Union[str, Any]							=     torch.Size([1, 3, 800, 1_066]    )
     self.assertEqual(encoding["pixel_values"].shape							,__lowerCAmelCase    )

     _lowerCamelCase   :				Union[str, Any]							=     torch.tensor([0.27_96, 0.31_38, 0.34_81]    )
     self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,__lowerCAmelCase							,atol=1e-4    )    )

     # verify area
     _lowerCamelCase   :				Any							=     torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]    )
     self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,__lowerCAmelCase    )    )
     # verify boxes
     _lowerCamelCase   :				int							=     torch.Size([6, 4]    )
     self.assertEqual(encoding["labels"][0]["boxes"].shape							,__lowerCAmelCase    )
     _lowerCamelCase   :				str							=     torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]    )
     self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,__lowerCAmelCase							,atol=1e-3    )    )
     # verify image_id
     _lowerCamelCase   :				Any							=     torch.tensor([39_769]    )
     self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,__lowerCAmelCase    )    )
     # verify is_crowd
     _lowerCamelCase   :				List[Any]							=     torch.tensor([0, 0, 0, 0, 0, 0]    )
     self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,__lowerCAmelCase    )    )
     # verify class_labels
     _lowerCamelCase   :				Any							=     torch.tensor([17, 17, 63, 75, 75, 93]    )
     self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,__lowerCAmelCase    )    )
     # verify masks
     _lowerCamelCase   :				str							=     822_873
     self.assertEqual(encoding["labels"][0]["masks"].sum().item()							,__lowerCAmelCase    )
     # verify orig_size
     _lowerCamelCase   :				List[str]							=     torch.tensor([480, 640]    )
     self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,__lowerCAmelCase    )    )
     # verify size
     _lowerCamelCase   :				Union[str, Any]							=     torch.tensor([800, 1_066]    )
     self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,__lowerCAmelCase    )    )
import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
__a    :	Union[str, Any]         =    logging.get_logger(__name__)





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)
				if "model" in sd.keys():
								__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)['''model''']

				# pop unnecessary weights
				__lowercase					     =				[
				    '''decoder.version''',
				    '''decoder.output_projection.weight''',
				]
				for key in keys_to_delete:
								if key in sd:
												sd.pop(lowercase				)

				__lowercase					     =				{
				    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
				    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
				    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
				    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
				}
				for old_key, new_key in keys_to_rename.items():
								if old_key in sd:
												__lowercase					     =				sd.pop(lowercase				)

				__lowercase					     =				list(sd.keys()				)
				for key in keys:
								if ".qkv_proj." in key:
												__lowercase					     =				sd[key]
												# We split QKV in separate Q,K,V

												__lowercase					     =				key.replace('''.qkv_proj.'''				,     '''.q_proj.'''				)
												__lowercase					     =				key.replace('''.qkv_proj.'''				,     '''.k_proj.'''				)
												__lowercase					     =				key.replace('''.qkv_proj.'''				,     '''.v_proj.'''				)

												__lowercase					     =				value.shape[0]
												assert depth % 3 == 0
												# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
												# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
												__lowercase     ,	__lowercase     ,	__lowercase					     =				torch.split(lowercase				,     depth // 3				,     dim=0				)

												__lowercase					     =				q
												__lowercase					     =				k
												__lowercase					     =				v
												del sd[key]

				return sd





@torch.no_grad()
def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase=None				):







				"""simple docstring"""



				__lowercase					     =				load_checkpoint(lowercase				)

				if config is not None:
								__lowercase					     =				OPTConfig.from_pretrained(lowercase				)
				else:
								__lowercase					     =				OPTConfig()

				__lowercase					     =				OPTModel(lowercase				).half().eval()
				model.load_state_dict(lowercase				)

				# Check results
				Path(lowercase				).mkdir(exist_ok=lowercase				)
				model.save_pretrained(lowercase				)


if __name__ == "__main__":
		__a    :	int         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--fairseq_path""",
		    type=str,
		    help=(
		        """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
		        """ https://huggingface.co/models?other=opt_metasq"""
		    ),
		)
		parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
		parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
		__a    :	Any         =    parser.parse_args()
		convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)